Modularly built, complete, coordinate- and template-free brain atlases

模块化构建、完整、无坐标和模板的大脑图谱

• 批准号: 10570256
• 负责人: Hang Lu
• 金额: $ 65.97万
• 依托单位: GEORGIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-10 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10570256
• 关键词: Address; Adult; Age; Aging; Algorithms; Amaze; Anatomy; Animals; Atlases; Benchmarking; Biological; Brain; Caenorhabditis elegans; Calibration; Cells; Collection; Complex; Data; Data Set; Democracy; Development; Disease; Ensure; Equipment; Functional Imaging; Future; Gene Expression Profiling; Genetic; Goals; Graph; Hand; Hermaphroditism; Human; Image; Individual; Individual Differences; Knowledge; Label; Laboratories; Larva; Letters; Literature; Machine Learning; Manuals; Maps; Methods; Modeling; Molecular; Nature; Nervous System; Neuronal Differentiation; Neurons; Organ; Organism; Outcome; Output; Phenotype; Population; Production; Reporter; Resolution; Sampling; Standardization; Stereotyping; Structure; System; Techniques; Tissues; Transgenic Animals; Transgenic Organisms; Update; Work; computational pipelines; crowdsourcing; design; digital; experimental study; flexibility; graph theory; imaging biomarker; imaging modality; improved; in vivo; in vivo imaging; individual variation; innovation; instrument; interest; large datasets; machine learning algorithm; medical specialties; model organism; neural circuit; neurodevelopment; prevent; sex; tool; young adult

Project Summary Anatomical atlases are spatial reference maps of cells in tissues/organs/brains and provide structure information for a wide range of biological analyses. The anatomical atlas of C. elegans nervous system is the only atlas for the entire nervous system of an animal with a resolution of all neuronal classes. However, built on a limited dataset and manual annotations, the standard atlas is insufficient in capturing biological variabilities, inaccurate and difficult to use for cell identification routinely, and only applicable for wildtype adult. While several heroic efforts of generating and imaging marker strains to build atlases have much improved the atlases, there is still a need for a pipeline to build accurate genetic-background-specific (or experimental-condition-specific) atlases easily and cheaply; further, there is a need to build such atlases that can be used without specialized equipment and with as few genetic perturbations as possible. Recent development of machine learning techniques and molecular transgenic approaches enabling the systematic production of in vivo reporters and imaging methods capable of collecting and processing high-resolution datasets at a large scale. The goal of this application is to address the current bottleneck by establishing a combined experimental and computational pipeline for modularly built, complete, coordinate- and template-free brain atlases for democratized and flexible uses. By imaging in vivo markers in a large number of live animals, the project will generate complete anatomical atlases for the C. elegans nervous system that capture variability in the population, which will greatly enhance the accuracy of identity predictions when used on each animal. The project will generate a collection of transgenic animals expressing partly overlapping in vivo markers that cover all neurons and build a computational pipeline to assemble the atlases. Further, a few widely applicable developmental atlases as a direct output of the project will showcase the pipeline and the approach. Importantly, the atlases do not seek to provide a set of rigid coordinates for each neuron class, but instead, a set of constraints that can be used to provide best estimates of neuron identities for each new sample. This ensures accuracy and applicability of the atlases to specific use case. The building of whole-brain atlases is piece-wise from easily-obtained partial atlases, and can be crowd-sourced if desired. The use will be streamlined with image input and neuron-identity prediction output. The proposed project is innovative, because it will build the first complete anatomical atlases of a nervous system using large datasets collected from in vivo markers of many live animals; it uses relational information uniquely suited to provide more accurate assignments; it will capture variabilities among individual animals. The proposed the work is significant, because it will address the urgent and unmet need for accurate, easy-to-use and easily updatable atlases to curate the brain; further, it will develop and establish conceptual framework and techniques for similar efforts in more complex anatomical systems.

项目摘要 解剖图谱是组织/器官/大脑中细胞的空间参考图， 为广泛的生物分析提供信息。并绘制了C.线虫的神经系统是 只有一个地图集的整个神经系统的动物与分辨率的所有神经元类。然而，建立在 有限的数据集和手动注释，标准图谱不足以捕获生物学变异性， 不准确且难以常规用于细胞鉴定，且仅适用于野生型成人。虽然若干 为了建立地图集，人们做出了巨大的努力来产生和成像标记菌株，这大大改善了地图集， 仍然需要一个管道来建立准确的遗传背景特异性（或实验条件特异性） 地图集容易和便宜;此外，有必要建立这样的地图集，可以使用，而无需专门的 设备和尽可能少的遗传干扰。机器学习的最新发展 技术和分子转基因方法，能够系统地产生体内报告基因， 能够大规模收集和处理高分辨率数据集的成像方法。这个目标 应用程序是通过建立一个结合实验和计算的解决目前的瓶颈， 模块化构建，完整，无坐标和无模板的大脑图谱的管道， 灵活使用。通过对大量活体动物体内标记物进行成像，该项目将产生完整的 C.解剖图谱线虫的神经系统，捕捉种群的变异性，这将大大 当用于每只动物时，提高身份预测的准确性。该项目将生成一个 转基因动物表达部分重叠的体内标记，覆盖所有神经元，并建立一个 计算管道来组装地图集。此外，一些广泛适用的发展地图集作为一个 项目的直接产出将展示管道和方法。重要的是，地图集并不寻求 为每个神经元类提供一组刚性坐标，而是一组可用于 为每个新样本提供神经元身份的最佳估计。这确保了准确性和适用性 将地图集应用于特定用例。全脑地图集的建立是从容易获得的部分脑区逐段进行的， 地图集，如果需要的话，可以众包。使用将与图像输入和神经元身份精简 预测输出。拟议的项目是创新的，因为它将建立第一个完整的解剖图谱 使用从许多活体动物的体内标记物收集的大型数据集来描述神经系统;它使用关系型 适合提供更准确分配的独特信息;它将捕获个体之间的差异 动物拟议的这项工作意义重大，因为它将解决对准确、 易于使用和易于更新的地图集，以策展大脑;此外，它将开发和建立概念 在更复杂的解剖系统中进行类似工作的框架和技术。